finres

1. Loading, pre-processing, analyzing and visualizing data

• 1. Load file: “IIGF_ETH_ERA5_19952021_ALL.fst” – reading data in fst format requires installing the fst package

ft <- read_fst("IIGF_ETH_ERA5_19952021_ALL.fst") 

summary(ft$GID_2)

##    Length     Class      Mode 
##    586920 character character

u_gid<-length(unique(ft$GID_2))
u_list<-unique(ft$GID_2)

• The number of individual regions available – GID_2 column is 60.

• 3. Create a subset of data for the region (GID_2 column): “ETH.8.14_1”

G1c<-ft%>%filter(GID_2 == "ETH.8.14_1")

• 4. Add the month number for every date (date column) in the subset

G1c<-G1c %>% mutate(month = month(date))

• 5. Compute mean monthly temperature (tas column) for every year available in the subset.

G1c<-G1c%>%group_by(month)%>%mutate(mean_temp_monthly=mean(tas))

• 6. Plot mean monthly temperature for every year: in x-axis month & year and y-axis the mean temperature.

Proceed following the same steps c. to f. for a random sample of 10 regions in the database and plot the results.

2. Running a regression analysis, testing and explaining the results

Run a panel regression in R (plm package